class_docstr: # Parameters
  
  seed_word_pairs_file : `Union[PathLike, str]`
      Path of file containing seed word pairs.
  tokenizer : `Tokenizer`
      Tokenizer used to tokenize seed words.
  direction_vocab : `Vocabulary`, optional (default=`None`)
      Vocabulary of tokenizer. If `None`, assumes tokenizer is of
      type `PreTrainedTokenizer` and uses tokenizer's `vocab` attribute.
  namespace : `str`, optional (default=`"tokens"`)
      Namespace of direction_vocab to use when tokenizing.
      Disregarded when direction_vocab is `None`.
  requires_grad : `bool`, optional (default=`False`)
      Option to enable gradient calculation for bias direction.
  noise : `float`, optional (default=`1e-10`)
      To avoid numerical instability if embeddings are initialized uniformly.
code: def __call__(self, module):
      # embed subword token IDs and mean pool to get
      # embedding of original word
      ids1_embeddings = []
      for i in self.ids1:
          i = i.to(module.weight.device)
          ids1_embeddings.append(
              torch.mean(module.forward(i), dim=0, keepdim=True)
          )
      ids2_embeddings = []
      for i in self.ids2:
          i = i.to(module.weight.device)
          ids2_embeddings.append(
              torch.mean(module.forward(i), dim=0, keepdim=True)
          )
      ids1_embeddings = torch.cat(ids1_embeddings)
      ids2_embeddings = torch.cat(ids2_embeddings)
  
      ids1_embeddings = self.add_noise(ids1_embeddings)
      ids2_embeddings = self.add_noise(ids2_embeddings)
  
      return self.direction(ids1_embeddings, ids2_embeddings)
  
imports: import torch
  from typing import Union, Optional
  from os import PathLike
  from allennlp.fairness.bias_direction import BiasDirection, PCABiasDirection, PairedPCABiasDirection, TwoMeansBiasDirection, ClassificationNormalBiasDirection
  from allennlp.fairness.bias_utils import load_word_pairs, load_words
  from allennlp.common import Registrable
  from allennlp.data.tokenizers.tokenizer import Tokenizer
  from allennlp.data import Vocabulary
label_desc: Use image node_id 2 for calling the TwoMeansBiasDirectionWrapper obj's underlying member method code with example usage: obj.__call__(module) and returns: self